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Journal of Software:2018.29(2):506-523

集合模拟可视化进展
舒清雅,刘日晨,洪帆,张江,袁晓如
(机器感知与智能教育部重点实验室(北京大学), 北京 100871;北京大学 信息科学技术学院, 北京 100871;机器感知与智能教育部重点实验室(北京大学), 北京 100871;北京大学 信息科学技术学院, 北京 100871;北京市虚拟仿真与可视化工程技术研究中心(北京大学), 北京 100871)
State-of-the-Art of Ensemble Visualization
SHU Qing-Ya,LIU Ri-Chen,HONG Fan,ZHANG Jiang,YUAN Xiao-Ru
(Key Laboratory of Machine Perception(Peking University), Ministry of Education, Beijing 100871, China;School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;Key Laboratory of Machine Perception(Peking University), Ministry of Education, Beijing 100871, China;School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;Beijing Engineering Technology Research Center of Virtual Simulation and Visualization(Peking University), Beijing 100871, China)
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Received:October 15, 2016    Revised:January 22, 2017
> 中文摘要: 近年来,集合模拟被频繁地运用于气候、数学、物理等领域.集合模拟数据通常具有多值、多变量、时变的属性,再加上其庞大的数据量,对这类数据的分析充满着挑战.集合模拟数据可视化是通过视觉和人机交互的手段,向领域专家揭示集合模拟数据中的成员差异和整体概况,从而帮助专家探索、总结和验证科学发现.从比较个体成员和概括整体成员这两个不同的分析任务,以及基于位置和基于特征这两种分析策略的角度,系统地分析了具有代表性的集合模拟可视化工作,收集并整理了各类方法的可视化形式、交互技术、应用案例.通过总结近几年的集合模拟可视化方法来讨论现有研究的趋势,并对未来研究做进一步的展望.
Abstract:Ensemble simulation is increasingly popular in scientific domain such as climate research, weather report, mathematics and physics. Ensemble simulation data sets are usually multi-valued, multi-variate, time-variant and large in scale. Thus, analyzing such data sets is challenging. Ensemble visualization helps scientists to compare ensemble members and give overall summary to the whole data sets by utilizing visual encoding and human interaction. It thus helps scientists to explore, conclude and validate their findings. This article describes analytical tasks and strategies for organizing existing works on visualization and visual analysis on ensemble simulation data sets. The analytical tasks for ensemble simulation data sets include comparing individual members and summarizing whole ensemble, whereas the analytical strategies consist of location-based method and feature-based method. This article reviews major works in ensemble visualization. It gives explanation to their visual design, interaction approaches and application scenarios, along with a discussion of recent trends and future research directions.
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基金项目:国家自然科学基金(61672055,61702271);国家重点基础研究发展计划(973)(2015CB352503);国家重点研发计划(2016QY02D0304) 国家自然科学基金(61672055,61702271);国家重点基础研究发展计划(973)(2015CB352503);国家重点研发计划(2016QY02D0304)
Foundation items:National Natural Science Foundation of China (61672055, 61702271); National Program on Key Basic Research Project of China (973) (2015CB352503); National Key Research and Development Program of China (2016QY02D0304)
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舒清雅,刘日晨,洪帆,张江,袁晓如.集合模拟可视化进展.软件学报,2018,29(2):506-523

SHU Qing-Ya,LIU Ri-Chen,HONG Fan,ZHANG Jiang,YUAN Xiao-Ru.State-of-the-Art of Ensemble Visualization.Journal of Software,2018,29(2):506-523